The kdevine package is no longer actively developed. Consider using - the kde1d package for marginal estimation, - the functions
vine()
andvinecop()
from the rvinecopulib package as replacements forkdevine()
andkdevinecop()
.
This package implements a vine copula based kernel density estimator. The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications (see, Nagler and Czado, 2016). The package is built on top of the copula density estimators in kdecopula and let’s you choose from all its implemented methods. The package can handle discrete and categorical data via continuous convolution.
You can install:
install.packages("kdevine")
A detailed description of of all functions and options can be found in the API documentaion. In short, the package provides the following functionality:
Class kdevine
and its methods:
kdevine()
: Multivariate kernel density estimation based on
vine copulas. Implements the estimator of (see, Nagler and
Czado, 2016).
dkdevine()
, rkdevine()
: Density and simulation functions.
Class kdevinecop
and its methods:
kdevinecop()
: Kernel estimator for the vine copula density
(see, Nagler and Czado, 2016).
dkdevinecop()
, rkdevinecop()
: Density and simulation
functions.
contour.kdevinecop()
: Matrix of contour plots of all
pair-copulas.
Class kde1d
and its methods:
kde1d()
: Univariate kernel density estimation for bounded and
unbounded support.
dke1d()
, pkde1d()
, rkde1d()
: Density, cdf, and simulation
functions.
plot.kde1d()
, lines.kde1d()
: Plots the estimated density.
Nagler, T., Czado, C. (2016) Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas Journal of Multivariate Analysis 151, 69-89 [preprint]
Nagler, T., Schellhase, C. and Czado, C. (2017) Nonparametric estimation of simplified vine copula models: comparison of methods Dependence Modeling, 5:99-120 [preprint]
Nagler, T. (2018) A generic approach to nonparametric function estimation with mixed data Statistics & Probability Letters, 137:326–330 [preprint]
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